Load and preprocess BH3 profiling data

Load

Use baseline level from DBP profiling

Prepare sample background annotations

Data structure

Data dimension

Number of samples

## [1] 73

Overview of CytC release data

For each concentration

For AUC

Dose response curves

Hierarchical clustering and heatmaps

Pricipal component analysis

PCA biplot

Associations between PCs and patient background

Associations with P-value < 0.05

## # A tibble: 4 × 3
## # Groups:   PC, feature [4]
##   PC    feature             p.value
##   <chr> <chr>                 <dbl>
## 1 PC2   NOTCH1              0.00129
## 2 PC2   IGHV.status         0.00286
## 3 PC1   trisomy12           0.0102 
## 4 PC2   Methylation_Cluster 0.0356

Plot associations

Plot feature loadings on the first three PCs

Feature correlations

AUC

Individual concentrations

Association with the spontaneous apoptosis of CLL cells

Measured by image data

PCs

Individual peptide

If multiple concentrations are identified as significant, only show the most significant concentration.

Association test with patient genomic background

Prepare patient genomic background

## [1] "IGHV.status" "del11q"      "del13q"      "del17p"      "trisomy12"   "NOTCH1"      "SF3B1"       "TP53"

Plot to summarise genomic background

Association test

Test for Genomics

Methylation cluster

Table of associations

P value scatter plot

Box plots for the significant associations (10% FDR)

If multiple concentrations are identified as significant, only show the most significant concentration.

Genomic correlation with individual concentrations

Association test

Summary heatmap plot for all concentrations

Associations with pretreatment status

Test for Genomics

## # A tibble: 7 × 4
##   feature p.value estimate p.adj
##   <chr>     <dbl>    <dbl> <dbl>
## 1 ABT199   0.0174  11.7    0.122
## 2 BAD      0.0460   9.51   0.161
## 3 BIM      0.414    2.99   0.580
## 4 FS1      0.190    4.23   0.333
## 5 HRKy     0.946   -0.0745 0.946
## 6 MS1      0.787    1.49   0.918
## 7 PUMA     0.116    6.85   0.270

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  patAnno$IGHV.status and patAnno$pretreat
## X-squared = 4.9003, df = 1, p-value = 0.02685

Estimate the confounding effect of pretreatment status

How many treated and untreated samples?

## 
##  no yes 
##  55  18

Adjust for pretreatment status

Test for Genomics

Methylation cluster

Combine

Test only in untreated

Test for Genomics

Methylation cluster

Table for comparing results

Association with transcriptomics

Preprocessing

RNAseq

BH3 profiling

Association test for each feature

Number of significant associations per feature (10% FDR)

## # A tibble: 7 × 2
##   feature     n
##   <chr>   <int>
## 1 ABT199    110
## 2 BAD        64
## 3 BIM         0
## 4 FS1         0
## 5 HRKy        0
## 6 MS1         0
## 7 PUMA        0

Table of significant associations

Heamap for significant correlations

Plot top 9 significant associations

Plot top 9 significant associations for ABT199

Plot selected associations for ABT199

Plot selected associations for BAD

Combine plot for ABT199 and BAD

Pathway enrichment analysis for features that associate with gene expression

Cancer Hallmark

KEGG

Record siginificant RNAs for later feature selection

Gene set heatmap

Association with proteomics

Preprocessing

Proteomics

## [1] 3314   30

BH3 profiling

Association test for each feature

Number of significant associations per feature (10% FDR)

None passed 10% FDR

Table of significant associations (P<0.01)

Plot top 9 associations

Multivariate feature selection for explaining BH3 profling in CLL

Data pre-processing

BH3 profiling

RNAseq

For genomic data

For demographic and clinical data

Function to Generate the explanatory dataset for each BH3 profile

Clean and integrate multi-omics data

Perform multi-variate regression

LASSO model

Training models

Function for multi-variate regression

Perform lasso regression

Ploting heatmap of selected features

Function for the heatmap plot

Plot all heatmaps